Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations3434
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory456.1 KiB
Average record size in memory136.0 B

Variable types

Numeric8
Categorical4
DateTime2
Text2

Alerts

Dropoff ID is highly overall correlated with Dropoff Longitude and 1 other fieldsHigh correlation
Dropoff Latitude is highly overall correlated with district_dropoffHigh correlation
Dropoff Longitude is highly overall correlated with Dropoff ID and 1 other fieldsHigh correlation
Passenger status is highly overall correlated with StatusHigh correlation
Pickup ID is highly overall correlated with Pickup Longitude and 1 other fieldsHigh correlation
Pickup Latitude is highly overall correlated with district_pickupHigh correlation
Pickup Longitude is highly overall correlated with Pickup ID and 1 other fieldsHigh correlation
Status is highly overall correlated with Passenger statusHigh correlation
district_dropoff is highly overall correlated with Dropoff ID and 2 other fieldsHigh correlation
district_pickup is highly overall correlated with Pickup ID and 2 other fieldsHigh correlation
Booking ID has unique values Unique
Pickup ID has 680 (19.8%) zeros Zeros
Dropoff ID has 616 (17.9%) zeros Zeros

Reproduction

Analysis started2024-11-13 20:36:07.506026
Analysis finished2024-11-13 20:36:17.172214
Duration9.67 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Booking ID
Real number (ℝ)

Unique 

Distinct3434
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260478.42
Minimum241740
Maximum270349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:17.330182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum241740
5-th percentile249425.65
Q1256348.25
median260749
Q3265275.25
95-th percentile269100.8
Maximum270349
Range28609
Interquartile range (IQR)8927

Descriptive statistics

Standard deviation5936.3035
Coefficient of variation (CV)0.022790001
Kurtosis-0.14757498
Mean260478.42
Median Absolute Deviation (MAD)4471
Skewness-0.48370519
Sum8.9448289 × 108
Variance35239699
MonotonicityNot monotonic
2024-11-13T21:36:17.520214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253212 1
 
< 0.1%
265273 1
 
< 0.1%
260719 1
 
< 0.1%
264040 1
 
< 0.1%
265006 1
 
< 0.1%
264352 1
 
< 0.1%
265196 1
 
< 0.1%
251907 1
 
< 0.1%
265201 1
 
< 0.1%
265445 1
 
< 0.1%
Other values (3424) 3424
99.7%
ValueCountFrequency (%)
241740 1
< 0.1%
241741 1
< 0.1%
241742 1
< 0.1%
242097 1
< 0.1%
242098 1
< 0.1%
242540 1
< 0.1%
242944 1
< 0.1%
242945 1
< 0.1%
242994 1
< 0.1%
242996 1
< 0.1%
ValueCountFrequency (%)
270349 1
< 0.1%
270342 1
< 0.1%
270338 1
< 0.1%
270330 1
< 0.1%
270328 1
< 0.1%
270327 1
< 0.1%
270320 1
< 0.1%
270319 1
< 0.1%
270318 1
< 0.1%
270317 1
< 0.1%

Status
Categorical

High correlation 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Validated
1999 
Cancelled by client
1024 
Cancelled by driver
399 
Changed by admin
 
6
Changed by client
 
4

Length

Max length24
Median length9
Mean length13.174141
Min length9

Characters and Unicode

Total characters45240
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCancelled by client
2nd rowCancelled by client
3rd rowCancelled by driver
4th rowValidated
5th rowCancelled by driver

Common Values

ValueCountFrequency (%)
Validated 1999
58.2%
Cancelled by client 1024
29.8%
Cancelled by driver 399
 
11.6%
Changed by admin 6
 
0.2%
Changed by client 4
 
0.1%
Cancelled by call center 2
 
0.1%

Length

2024-11-13T21:36:17.692182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T21:36:17.838181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
validated 1999
31.7%
by 1435
22.8%
cancelled 1425
22.6%
client 1028
16.3%
driver 399
 
6.3%
changed 10
 
0.2%
admin 6
 
0.1%
call 2
 
< 0.1%
center 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 6290
13.9%
l 5881
13.0%
d 5838
12.9%
a 5441
12.0%
i 3432
7.6%
t 3029
6.7%
2872
6.3%
n 2471
 
5.5%
c 2457
 
5.4%
V 1999
 
4.4%
Other values (8) 5530
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6290
13.9%
l 5881
13.0%
d 5838
12.9%
a 5441
12.0%
i 3432
7.6%
t 3029
6.7%
2872
6.3%
n 2471
 
5.5%
c 2457
 
5.4%
V 1999
 
4.4%
Other values (8) 5530
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6290
13.9%
l 5881
13.0%
d 5838
12.9%
a 5441
12.0%
i 3432
7.6%
t 3029
6.7%
2872
6.3%
n 2471
 
5.5%
c 2457
 
5.4%
V 1999
 
4.4%
Other values (8) 5530
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6290
13.9%
l 5881
13.0%
d 5838
12.9%
a 5441
12.0%
i 3432
7.6%
t 3029
6.7%
2872
6.3%
n 2471
 
5.5%
c 2457
 
5.4%
V 1999
 
4.4%
Other values (8) 5530
12.2%

Passenger status
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Trip completed
1999 
Cancelled
1435 

Length

Max length14
Median length14
Mean length11.9106
Min length9

Characters and Unicode

Total characters40901
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCancelled
2nd rowCancelled
3rd rowCancelled
4th rowTrip completed
5th rowCancelled

Common Values

ValueCountFrequency (%)
Trip completed 1999
58.2%
Cancelled 1435
41.8%

Length

2024-11-13T21:36:18.004216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T21:36:18.120216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
trip 1999
36.8%
completed 1999
36.8%
cancelled 1435
26.4%

Most occurring characters

ValueCountFrequency (%)
e 6868
16.8%
l 4869
11.9%
p 3998
9.8%
c 3434
8.4%
d 3434
8.4%
T 1999
 
4.9%
r 1999
 
4.9%
i 1999
 
4.9%
1999
 
4.9%
o 1999
 
4.9%
Other values (5) 8303
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40901
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6868
16.8%
l 4869
11.9%
p 3998
9.8%
c 3434
8.4%
d 3434
8.4%
T 1999
 
4.9%
r 1999
 
4.9%
i 1999
 
4.9%
1999
 
4.9%
o 1999
 
4.9%
Other values (5) 8303
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40901
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6868
16.8%
l 4869
11.9%
p 3998
9.8%
c 3434
8.4%
d 3434
8.4%
T 1999
 
4.9%
r 1999
 
4.9%
i 1999
 
4.9%
1999
 
4.9%
o 1999
 
4.9%
Other values (5) 8303
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40901
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6868
16.8%
l 4869
11.9%
p 3998
9.8%
c 3434
8.4%
d 3434
8.4%
T 1999
 
4.9%
r 1999
 
4.9%
i 1999
 
4.9%
1999
 
4.9%
o 1999
 
4.9%
Other values (5) 8303
20.3%

Passengers
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2673267
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:18.232181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.76376466
Coefficient of variation (CV)0.60265805
Kurtosis24.258398
Mean1.2673267
Median Absolute Deviation (MAD)0
Skewness4.2956108
Sum4352
Variance0.58333646
MonotonicityNot monotonic
2024-11-13T21:36:18.411178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 2872
83.6%
2 373
 
10.9%
3 97
 
2.8%
4 55
 
1.6%
5 20
 
0.6%
8 8
 
0.2%
7 5
 
0.1%
6 4
 
0.1%
ValueCountFrequency (%)
1 2872
83.6%
2 373
 
10.9%
3 97
 
2.8%
4 55
 
1.6%
5 20
 
0.6%
6 4
 
0.1%
7 5
 
0.1%
8 8
 
0.2%
ValueCountFrequency (%)
8 8
 
0.2%
7 5
 
0.1%
6 4
 
0.1%
5 20
 
0.6%
4 55
 
1.6%
3 97
 
2.8%
2 373
 
10.9%
1 2872
83.6%

Pickup ID
Real number (ℝ)

High correlation  Zeros 

Distinct64
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.654339
Minimum0
Maximum68
Zeros680
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:18.616215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median21
Q331
95-th percentile55.7
Maximum68
Range68
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.206847
Coefficient of variation (CV)0.80368035
Kurtosis-0.52387025
Mean22.654339
Median Absolute Deviation (MAD)13
Skewness0.45980502
Sum77795
Variance331.48928
MonotonicityNot monotonic
2024-11-13T21:36:18.791213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 680
19.8%
30 265
 
7.7%
19 226
 
6.6%
31 161
 
4.7%
8 160
 
4.7%
21 159
 
4.6%
29 142
 
4.1%
28 128
 
3.7%
40 101
 
2.9%
50 88
 
2.6%
Other values (54) 1324
38.6%
ValueCountFrequency (%)
0 680
19.8%
1 72
 
2.1%
2 20
 
0.6%
3 29
 
0.8%
4 70
 
2.0%
5 8
 
0.2%
6 37
 
1.1%
7 6
 
0.2%
8 160
 
4.7%
9 21
 
0.6%
ValueCountFrequency (%)
68 2
 
0.1%
67 29
 
0.8%
66 22
 
0.6%
65 6
 
0.2%
64 73
2.1%
63 5
 
0.1%
62 8
 
0.2%
61 1
 
< 0.1%
59 17
 
0.5%
58 1
 
< 0.1%

Dropoff ID
Real number (ℝ)

High correlation  Zeros 

Distinct62
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.405649
Minimum0
Maximum68
Zeros616
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:18.964179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median21
Q336
95-th percentile62
Maximum68
Range68
Interquartile range (IQR)29

Descriptive statistics

Standard deviation18.618543
Coefficient of variation (CV)0.79547218
Kurtosis-0.57838156
Mean23.405649
Median Absolute Deviation (MAD)15
Skewness0.47844504
Sum80375
Variance346.65014
MonotonicityNot monotonic
2024-11-13T21:36:19.140182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 616
17.9%
19 217
 
6.3%
15 180
 
5.2%
30 177
 
5.2%
31 146
 
4.3%
40 141
 
4.1%
8 135
 
3.9%
21 130
 
3.8%
28 120
 
3.5%
29 115
 
3.3%
Other values (52) 1457
42.4%
ValueCountFrequency (%)
0 616
17.9%
1 74
 
2.2%
2 8
 
0.2%
3 32
 
0.9%
4 65
 
1.9%
5 8
 
0.2%
6 48
 
1.4%
7 19
 
0.6%
8 135
 
3.9%
9 28
 
0.8%
ValueCountFrequency (%)
68 1
 
< 0.1%
67 41
1.2%
66 25
 
0.7%
65 10
 
0.3%
64 87
2.5%
63 7
 
0.2%
62 5
 
0.1%
61 2
 
0.1%
59 17
 
0.5%
57 12
 
0.3%
Distinct2870
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Minimum2024-09-01 08:18:00
Maximum2024-09-30 21:46:00
2024-11-13T21:36:19.313179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:19.494179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2915
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Minimum2024-09-01 08:34:00
Maximum2024-09-30 21:52:00
2024-11-13T21:36:19.660214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:19.835214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct64
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:20.035217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length31
Mean length23.055038
Min length7

Characters and Unicode

Total characters79171
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowIrfersdorf, Am Kirchplatz
2nd rowWiesenhofen, Kirche
3rd rowBeilngries, Frauenkirche
4th rowIrfersdorf, Am Kirchplatz
5th rowAschbuch, Waldsiedlung
ValueCountFrequency (%)
beilngries 1414
 
17.5%
kinding 787
 
9.8%
bahnhof 680
 
8.4%
straße 335
 
4.2%
frauenkirche 265
 
3.3%
ringstrasse 226
 
2.8%
strasse 220
 
2.7%
haunstetten 203
 
2.5%
gewerbegebiet 164
 
2.0%
eichstaetter 161
 
2.0%
Other values (82) 3602
44.7%
2024-11-13T21:36:20.418126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9560
 
12.1%
i 6266
 
7.9%
r 5978
 
7.6%
n 5976
 
7.5%
s 4882
 
6.2%
4623
 
5.8%
a 4436
 
5.6%
t 3962
 
5.0%
h 3454
 
4.4%
, 3342
 
4.2%
Other values (38) 26692
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9560
 
12.1%
i 6266
 
7.9%
r 5978
 
7.6%
n 5976
 
7.5%
s 4882
 
6.2%
4623
 
5.8%
a 4436
 
5.6%
t 3962
 
5.0%
h 3454
 
4.4%
, 3342
 
4.2%
Other values (38) 26692
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9560
 
12.1%
i 6266
 
7.9%
r 5978
 
7.6%
n 5976
 
7.5%
s 4882
 
6.2%
4623
 
5.8%
a 4436
 
5.6%
t 3962
 
5.0%
h 3454
 
4.4%
, 3342
 
4.2%
Other values (38) 26692
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9560
 
12.1%
i 6266
 
7.9%
r 5978
 
7.6%
n 5976
 
7.5%
s 4882
 
6.2%
4623
 
5.8%
a 4436
 
5.6%
t 3962
 
5.0%
h 3454
 
4.4%
, 3342
 
4.2%
Other values (38) 26692
33.7%

district_pickup
Categorical

High correlation 

Distinct27
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Beilngries
1415 
Kinding
787 
Haunstetten
203 
Paulushofen
 
116
Wolfsbuch
 
115
Other values (22)
798 

Length

Max length14
Median length13
Mean length9.3666278
Min length7

Characters and Unicode

Total characters32165
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowIrfersdorf
2nd rowLitterzhofen
3rd rowBeilngries
4th rowIrfersdorf
5th rowAschbuch

Common Values

ValueCountFrequency (%)
Beilngries 1415
41.2%
Kinding 787
22.9%
Haunstetten 203
 
5.9%
Paulushofen 116
 
3.4%
Wolfsbuch 115
 
3.3%
Irfersdorf 101
 
2.9%
Enkering 92
 
2.7%
Hirschberg 78
 
2.3%
Aschbuch 76
 
2.2%
Biberbach 73
 
2.1%
Other values (17) 378
 
11.0%

Length

2024-11-13T21:36:20.606092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beilngries 1415
41.2%
kinding 787
22.9%
haunstetten 203
 
5.9%
paulushofen 116
 
3.4%
wolfsbuch 115
 
3.3%
irfersdorf 101
 
2.9%
enkering 92
 
2.7%
hirschberg 78
 
2.3%
aschbuch 76
 
2.2%
biberbach 73
 
2.1%
Other values (17) 378
 
11.0%

Most occurring characters

ValueCountFrequency (%)
i 4818
15.0%
n 4086
12.7%
e 4024
12.5%
r 2519
 
7.8%
g 2459
 
7.6%
s 2261
 
7.0%
l 1681
 
5.2%
B 1514
 
4.7%
d 1080
 
3.4%
t 923
 
2.9%
Other values (26) 6800
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4818
15.0%
n 4086
12.7%
e 4024
12.5%
r 2519
 
7.8%
g 2459
 
7.6%
s 2261
 
7.0%
l 1681
 
5.2%
B 1514
 
4.7%
d 1080
 
3.4%
t 923
 
2.9%
Other values (26) 6800
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4818
15.0%
n 4086
12.7%
e 4024
12.5%
r 2519
 
7.8%
g 2459
 
7.6%
s 2261
 
7.0%
l 1681
 
5.2%
B 1514
 
4.7%
d 1080
 
3.4%
t 923
 
2.9%
Other values (26) 6800
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4818
15.0%
n 4086
12.7%
e 4024
12.5%
r 2519
 
7.8%
g 2459
 
7.6%
s 2261
 
7.0%
l 1681
 
5.2%
B 1514
 
4.7%
d 1080
 
3.4%
t 923
 
2.9%
Other values (26) 6800
21.1%

Pickup Latitude
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.016356
Minimum48.965578
Maximum49.069082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:20.852091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum48.965578
5-th percentile48.976827
Q148.992168
median49.019003
Q349.035227
95-th percentile49.043035
Maximum49.069082
Range0.103504
Interquartile range (IQR)0.043059

Descriptive statistics

Standard deviation0.023411519
Coefficient of variation (CV)0.00047762667
Kurtosis-1.0199412
Mean49.016356
Median Absolute Deviation (MAD)0.017909
Skewness-0.13487343
Sum168322.17
Variance0.00054809921
MonotonicityNot monotonic
2024-11-13T21:36:21.046092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.992168 680
19.8%
49.036378 265
 
7.7%
49.033832 226
 
6.6%
49.035227 161
 
4.7%
49.017505 160
 
4.7%
49.033525 159
 
4.6%
49.035103 142
 
4.1%
49.036912 128
 
3.7%
48.994215 101
 
2.9%
48.976827 88
 
2.6%
Other values (54) 1324
38.6%
ValueCountFrequency (%)
48.965578 17
 
0.5%
48.97066 22
 
0.6%
48.970778 23
 
0.7%
48.97449 27
 
0.8%
48.975027 13
 
0.4%
48.976207 21
 
0.6%
48.976818 4
 
0.1%
48.976827 88
2.6%
48.978988 19
 
0.6%
48.98216 36
1.0%
ValueCountFrequency (%)
49.069082 6
 
0.2%
49.068532 73
2.1%
49.064045 1
 
< 0.1%
49.058095 5
 
0.1%
49.058008 17
 
0.5%
49.054378 8
 
0.2%
49.048472 29
 
0.8%
49.044913 23
 
0.7%
49.043035 22
 
0.6%
49.041112 62
1.8%

Pickup Longitude
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.447205
Minimum11.361228
Maximum11.562987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:21.229124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.361228
5-th percentile11.377365
Q111.390002
median11.467885
Q311.474083
95-th percentile11.520783
Maximum11.562987
Range0.201759
Interquartile range (IQR)0.084081

Descriptive statistics

Standard deviation0.049676529
Coefficient of variation (CV)0.0043396209
Kurtosis-0.69206211
Mean11.447205
Median Absolute Deviation (MAD)0.016447
Skewness-0.1268469
Sum39309.702
Variance0.0024677576
MonotonicityNot monotonic
2024-11-13T21:36:21.412091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.377365 680
19.8%
11.470632 265
 
7.7%
11.471982 226
 
6.6%
11.467885 161
 
4.7%
11.404733 160
 
4.7%
11.475793 159
 
4.6%
11.474083 142
 
4.1%
11.471075 128
 
3.7%
11.461103 101
 
2.9%
11.559612 88
 
2.6%
Other values (54) 1324
38.6%
ValueCountFrequency (%)
11.361228 20
 
0.6%
11.363953 72
 
2.1%
11.377365 680
19.8%
11.38279 70
 
2.0%
11.38727 8
 
0.2%
11.390002 29
 
0.8%
11.404733 160
 
4.7%
11.40739 6
 
0.2%
11.408473 37
 
1.1%
11.412738 22
 
0.6%
ValueCountFrequency (%)
11.562987 27
 
0.8%
11.559612 88
2.6%
11.534193 13
 
0.4%
11.530053 19
 
0.6%
11.527642 1
 
< 0.1%
11.522882 2
 
0.1%
11.520783 59
1.7%
11.518182 2
 
0.1%
11.516585 17
 
0.5%
11.50951 17
 
0.5%
Distinct62
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:21.619126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length31
Mean length22.863425
Min length7

Characters and Unicode

Total characters78513
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowKinding, Bahnhof
2nd rowKinding, Bahnhof
3rd rowKinding, Bahnhof
4th rowBeilngries, Deutscher Hof
5th rowBeilngries, Kelheimer Straße
ValueCountFrequency (%)
beilngries 1242
 
15.6%
kinding 721
 
9.0%
bahnhof 616
 
7.7%
straße 332
 
4.2%
ringstrasse 217
 
2.7%
am 211
 
2.6%
haunstetten 202
 
2.5%
strasse 189
 
2.4%
hirschberg 182
 
2.3%
dorfkapelle 180
 
2.3%
Other values (82) 3882
48.7%
2024-11-13T21:36:22.027095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9181
 
11.7%
r 6058
 
7.7%
i 5817
 
7.4%
n 5620
 
7.2%
s 4831
 
6.2%
4540
 
5.8%
a 4444
 
5.7%
t 3779
 
4.8%
h 3390
 
4.3%
, 3315
 
4.2%
Other values (40) 27538
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9181
 
11.7%
r 6058
 
7.7%
i 5817
 
7.4%
n 5620
 
7.2%
s 4831
 
6.2%
4540
 
5.8%
a 4444
 
5.7%
t 3779
 
4.8%
h 3390
 
4.3%
, 3315
 
4.2%
Other values (40) 27538
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9181
 
11.7%
r 6058
 
7.7%
i 5817
 
7.4%
n 5620
 
7.2%
s 4831
 
6.2%
4540
 
5.8%
a 4444
 
5.7%
t 3779
 
4.8%
h 3390
 
4.3%
, 3315
 
4.2%
Other values (40) 27538
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9181
 
11.7%
r 6058
 
7.7%
i 5817
 
7.4%
n 5620
 
7.2%
s 4831
 
6.2%
4540
 
5.8%
a 4444
 
5.7%
t 3779
 
4.8%
h 3390
 
4.3%
, 3315
 
4.2%
Other values (40) 27538
35.1%

district_dropoff
Categorical

High correlation 

Distinct27
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Beilngries
1242 
Kinding
721 
Haunstetten
202 
Hirschberg
182 
Paulushofen
153 
Other values (22)
934 

Length

Max length14
Median length13
Mean length9.439138
Min length7

Characters and Unicode

Total characters32414
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowKinding
2nd rowKinding
3rd rowKinding
4th rowBeilngries
5th rowBeilngries

Common Values

ValueCountFrequency (%)
Beilngries 1242
36.2%
Kinding 721
21.0%
Haunstetten 202
 
5.9%
Hirschberg 182
 
5.3%
Paulushofen 153
 
4.5%
Irfersdorf 141
 
4.1%
Wolfsbuch 135
 
3.9%
Biberbach 87
 
2.5%
Enkering 82
 
2.4%
Pfraundorf 81
 
2.4%
Other values (17) 408
 
11.9%

Length

2024-11-13T21:36:22.432091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beilngries 1242
36.2%
kinding 721
21.0%
haunstetten 202
 
5.9%
hirschberg 182
 
5.3%
paulushofen 153
 
4.5%
irfersdorf 141
 
4.1%
wolfsbuch 135
 
3.9%
biberbach 87
 
2.5%
enkering 82
 
2.4%
pfraundorf 81
 
2.4%
Other values (17) 408
 
11.9%

Most occurring characters

ValueCountFrequency (%)
i 4460
13.8%
e 3928
12.1%
n 3852
11.9%
r 2729
 
8.4%
g 2304
 
7.1%
s 2300
 
7.1%
l 1572
 
4.8%
B 1351
 
4.2%
d 1075
 
3.3%
t 922
 
2.8%
Other values (26) 7921
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4460
13.8%
e 3928
12.1%
n 3852
11.9%
r 2729
 
8.4%
g 2304
 
7.1%
s 2300
 
7.1%
l 1572
 
4.8%
B 1351
 
4.2%
d 1075
 
3.3%
t 922
 
2.8%
Other values (26) 7921
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4460
13.8%
e 3928
12.1%
n 3852
11.9%
r 2729
 
8.4%
g 2304
 
7.1%
s 2300
 
7.1%
l 1572
 
4.8%
B 1351
 
4.2%
d 1075
 
3.3%
t 922
 
2.8%
Other values (26) 7921
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4460
13.8%
e 3928
12.1%
n 3852
11.9%
r 2729
 
8.4%
g 2304
 
7.1%
s 2300
 
7.1%
l 1572
 
4.8%
B 1351
 
4.2%
d 1075
 
3.3%
t 922
 
2.8%
Other values (26) 7921
24.4%

Dropoff Latitude
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.016306
Minimum48.965578
Maximum49.069082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:22.601091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum48.965578
5-th percentile48.976827
Q148.992168
median49.017505
Q349.035227
95-th percentile49.044913
Maximum49.069082
Range0.103504
Interquartile range (IQR)0.043059

Descriptive statistics

Standard deviation0.024015556
Coefficient of variation (CV)0.00048995035
Kurtosis-0.9855651
Mean49.016306
Median Absolute Deviation (MAD)0.019783
Skewness-0.086705413
Sum168321.99
Variance0.00057674694
MonotonicityNot monotonic
2024-11-13T21:36:22.777091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.992168 616
17.9%
49.033832 217
 
6.3%
49.03815 180
 
5.2%
49.036378 177
 
5.2%
49.035227 146
 
4.3%
48.994215 141
 
4.1%
49.017505 135
 
3.9%
49.033525 130
 
3.8%
49.036912 120
 
3.5%
49.035103 115
 
3.3%
Other values (52) 1457
42.4%
ValueCountFrequency (%)
48.965578 24
 
0.7%
48.97066 20
 
0.6%
48.970778 24
 
0.7%
48.97449 35
 
1.0%
48.975027 18
 
0.5%
48.976207 23
 
0.7%
48.976818 5
 
0.1%
48.976827 100
2.9%
48.978988 22
 
0.6%
48.98216 22
 
0.6%
ValueCountFrequency (%)
49.069082 10
 
0.3%
49.068532 87
2.5%
49.064045 2
 
0.1%
49.058095 7
 
0.2%
49.058008 17
 
0.5%
49.054378 5
 
0.1%
49.048472 41
1.2%
49.044913 42
1.2%
49.043035 25
 
0.7%
49.041112 69
2.0%

Dropoff Longitude
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.449012
Minimum11.361228
Maximum11.562987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.7 KiB
2024-11-13T21:36:22.938092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.361228
5-th percentile11.377365
Q111.404733
median11.467885
Q311.474083
95-th percentile11.530053
Maximum11.562987
Range0.201759
Interquartile range (IQR)0.06935

Descriptive statistics

Standard deviation0.049299018
Coefficient of variation (CV)0.0043059626
Kurtosis-0.5357013
Mean11.449012
Median Absolute Deviation (MAD)0.016447
Skewness-0.084846696
Sum39315.907
Variance0.0024303932
MonotonicityNot monotonic
2024-11-13T21:36:23.114089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.377365 616
17.9%
11.471982 217
 
6.3%
11.451488 180
 
5.2%
11.470632 177
 
5.2%
11.467885 146
 
4.3%
11.461103 141
 
4.1%
11.404733 135
 
3.9%
11.475793 130
 
3.8%
11.471075 120
 
3.5%
11.474083 115
 
3.3%
Other values (52) 1457
42.4%
ValueCountFrequency (%)
11.361228 8
 
0.2%
11.363953 74
 
2.2%
11.377365 616
17.9%
11.38279 65
 
1.9%
11.38727 8
 
0.2%
11.390002 32
 
0.9%
11.404733 135
 
3.9%
11.40739 10
 
0.3%
11.408473 48
 
1.4%
11.412738 25
 
0.7%
ValueCountFrequency (%)
11.562987 35
 
1.0%
11.559612 100
2.9%
11.534193 18
 
0.5%
11.53071 1
 
< 0.1%
11.530053 24
 
0.7%
11.522882 1
 
< 0.1%
11.520783 43
1.3%
11.518182 4
 
0.1%
11.516585 17
 
0.5%
11.50951 24
 
0.7%

Interactions

2024-11-13T21:36:15.605479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:08.038439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:09.092561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:10.097530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:11.093503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:12.207479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:13.170480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:14.671479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:15.722488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:08.144439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:09.214568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:10.235527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:11.213517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:12.328480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:13.292491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:14.787479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:15.845514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:08.260441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:09.334563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:10.371528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:11.338502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:12.451479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:13.876479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:14.904480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:15.965486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:08.380473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:09.465561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:10.487479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:11.474480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:12.574481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:14.005480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:15.025482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:16.088479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:08.498441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:09.592564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:10.604484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:11.620480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:12.698480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:14.159484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:15.147479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:16.233479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:08.603561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:09.705561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:10.713480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:11.746481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:12.806479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:14.286479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:15.255480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:16.438479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:08.791560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:09.834569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:10.850482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:11.931479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:12.936479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:14.419479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:15.375489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:16.550480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:08.972569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:09.955528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:10.964513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:12.073480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:13.045479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:14.540479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T21:36:15.479481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-13T21:36:23.243089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Booking IDDropoff IDDropoff LatitudeDropoff LongitudePassenger statusPassengersPickup IDPickup LatitudePickup LongitudeStatusdistrict_dropoffdistrict_pickup
Booking ID1.000-0.043-0.027-0.0420.0560.054-0.0630.006-0.0260.0530.1230.146
Dropoff ID-0.0431.0000.2960.7510.050-0.0080.009-0.068-0.0270.0330.8750.269
Dropoff Latitude-0.0270.2961.0000.1770.045-0.001-0.081-0.274-0.2080.0250.8760.274
Dropoff Longitude-0.0420.7510.1771.0000.0000.011-0.039-0.197-0.0830.0340.9110.271
Passenger status0.0560.0500.0450.0001.0000.0680.0630.1020.0880.9990.1050.133
Passengers0.054-0.008-0.0010.0110.0681.000-0.027-0.0260.0140.0410.0530.100
Pickup ID-0.0630.009-0.081-0.0390.063-0.0271.0000.3560.7420.0560.2830.868
Pickup Latitude0.006-0.068-0.274-0.1970.102-0.0260.3561.0000.2440.0670.2800.873
Pickup Longitude-0.026-0.027-0.208-0.0830.0880.0140.7420.2441.0000.0660.2630.908
Status0.0530.0330.0250.0340.9990.0410.0560.0670.0661.0000.0800.066
district_dropoff0.1230.8750.8760.9110.1050.0530.2830.2800.2630.0801.0000.178
district_pickup0.1460.2690.2740.2710.1330.1000.8680.8730.9080.0660.1781.000

Missing values

2024-11-13T21:36:16.747478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-13T21:36:17.044480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Booking IDStatusPassenger statusPassengersPickup IDDropoff IDActual Pickup TimeActual Dropoff Timename_pickupdistrict_pickupPickup LatitudePickup Longitudename_dropoffdistrict_dropoffDropoff LatitudeDropoff Longitude
1253212Cancelled by clientCancelled14002024-09-01 08:18:002024-09-01 08:34:00Irfersdorf, Am KirchplatzIrfersdorf48.99421511.461103Kinding, BahnhofKinding48.99216811.377365
2253369Cancelled by clientCancelled36602024-09-01 08:46:002024-09-01 09:09:00Wiesenhofen, KircheLitterzhofen49.04303511.412738Kinding, BahnhofKinding48.99216811.377365
3253808Cancelled by driverCancelled23002024-09-01 08:56:002024-09-01 09:09:00Beilngries, FrauenkircheBeilngries49.03637811.470632Kinding, BahnhofKinding48.99216811.377365
4253782ValidatedTrip completed240212024-09-01 09:14:002024-09-01 09:25:00Irfersdorf, Am KirchplatzIrfersdorf48.99421511.461103Beilngries, Deutscher HofBeilngries49.03352511.475793
5252382Cancelled by driverCancelled146222024-09-01 09:41:002024-09-01 09:49:00Aschbuch, WaldsiedlungAschbuch48.97620711.491877Beilngries, Kelheimer StraßeBeilngries49.03292811.479163
6251869ValidatedTrip completed32702024-09-01 09:54:002024-09-01 10:06:00Beilngries, HafenBeilngries49.04111211.471240Kinding, BahnhofKinding48.99216811.377365
7251083ValidatedTrip completed12092024-09-01 10:39:002024-09-01 10:46:00Beilngries, ZOBBeilngries49.03217311.476295Unteremmendorf, WendeschleifeUnteremmendorf48.99211211.434157
8253882Cancelled by clientCancelled138292024-09-01 10:29:002024-09-01 10:38:00Paulushofen, DorfstraßePaulushofen49.01033711.502283Beilngries, RathausBeilngries49.03510311.474083
9251191Cancelled by driverCancelled21902024-09-01 10:36:002024-09-01 10:55:00Beilngries, RingstrasseBeilngries49.03383211.471982Kinding, BahnhofKinding48.99216811.377365
10253916ValidatedTrip completed21902024-09-01 10:36:002024-09-01 10:52:00Beilngries, RingstrasseBeilngries49.03383211.471982Kinding, BahnhofKinding48.99216811.377365
Booking IDStatusPassenger statusPassengersPickup IDDropoff IDActual Pickup TimeActual Dropoff Timename_pickupdistrict_pickupPickup LatitudePickup Longitudename_dropoffdistrict_dropoffDropoff LatitudeDropoff Longitude
3827270318Cancelled by clientCancelled30642024-09-30 19:57:002024-09-30 20:20:00Kinding, BahnhofKinding48.99216811.377365Plankstetten, Biberbacher StraßeBiberbach49.06853211.455142
3828263021ValidatedTrip completed130402024-09-30 20:08:002024-09-30 20:14:00Beilngries, FrauenkircheBeilngries49.03637811.470632Irfersdorf, Am KirchplatzIrfersdorf48.99421511.461103
3829262045Cancelled by driverCancelled10182024-09-30 20:55:002024-09-30 21:07:00Kinding, BahnhofKinding48.99216811.377365Beilngries, UtzmuehlwegBeilngries49.03948311.467702
3830269620ValidatedTrip completed10292024-09-30 20:55:002024-09-30 21:06:00Kinding, BahnhofKinding48.99216811.377365Beilngries, RathausBeilngries49.03510311.474083
3831269717Cancelled by clientCancelled30282024-09-30 20:54:002024-09-30 21:09:00Kinding, BahnhofKinding48.99216811.377365Beilngries, Neumarkter StraßeBeilngries49.03691211.471075
3832270319Cancelled by driverCancelled30642024-09-30 20:55:002024-09-30 21:42:00Kinding, BahnhofKinding48.99216811.377365Plankstetten, Biberbacher StraßeBiberbach49.06853211.455142
3833268219ValidatedTrip completed1412024-09-30 21:20:002024-09-30 21:23:00Kinding, MarktplatzKinding49.00056211.382790Enkering, MaibaumEnkering48.99249811.363953
3834269630Cancelled by clientCancelled12102024-09-30 21:25:002024-09-30 21:33:00Enkering, FeuerwehrhausEnkering48.99365211.361228Pfraundorf, DorfplatzPfraundorf49.00514211.445253
3835269524Cancelled by clientCancelled119422024-09-30 21:37:002024-09-30 21:47:00Beilngries, RingstrasseBeilngries49.03383211.471982Grampersdorf, LandstrasseGrampersdorf48.97077811.476563
3836269863ValidatedTrip completed133152024-09-30 21:46:002024-09-30 21:52:00Beilngries, BauhofstraßeBeilngries49.03160811.461872Hirschberg, DorfkapelleHirschberg49.03815011.451488